Machine learning techniques include supervised learning and unsupervised learning. Supervised learning involves using labeled data to train a machine learning program (e.g., where the labeled data indicates the correct output), and unsupervised learning is performed without labeled data. In both supervised learning and unsupervised learning, machine learning programs are trained to perform operations, such as making predictions or decisions (e.g., to categorize data by assigning group association labels to the data).
Feature importance estimation can be used to understand operations performed by machine learning programs. For example, to understand why a machine learning program made a particular prediction or decision, features of input data can be analyzed (e.g., to reverse engineer why a particular element or sample of data was or was not classified a particular way). Feature importance estimation may also be used to improve existing models, such as by adjusting feature selection of the models.
Feature importance ranking may involve analyzing features associated with data and labels assigned to the data, such as by determining correlation (e.g., Pearson correlation or Spearman correlation) between features and labels associated with the data, measuring mutual information (or relative entropy) between features and labels associated with the data, or performing other techniques. To further illustrate, in some techniques, a local interpretable model-agnostic explanations (LIME) library or a Shapely additive explanation (SHAP) library can be used in connection with feature importance ranking. In some cases, these techniques may be ineffective or may produce poor results. For example, in unsupervised learning, no labels may be assigned to data.
As another example, in some cases, conventional approaches may provide “global” feature importance rankings that may fail to explain certain decisions made by a machine learning program. To illustrate, in some cases, data may include two data elements that are similar to one another (e.g., as measured within a feature space) but that are nonetheless assigned to different classifications by a machine learning program.
Conventional feature importance ranking techniques may explain why the two data elements are different from other data elements (e.g., by identifying common features between the data elements that are different from features of other data elements) but may fail to explain why data elements are classified differently by the machine learning program. For example, conventional feature importance ranking techniques may fail to explain why the two data elements are not assigned to the same cluster. Further, conventional feature importance estimation techniques may not adequately explain anomalous samples that are not associated with any label.
In some cases, conventional feature importance ranking techniques reduce efficiency of operation of an electronic device, such as by increasing power consumption by an electronic device. For example, electronic devices, communication networks, and other resources may be used to collect, transmit, store, and process irrelevant (or less relevant) data, such as data that is not used by a machine learning program to make a prediction or a decision. The collection, transmission, storage, and processing of irrelevant (or less relevant) data uses device power as well as resources that could otherwise be utilized to collect, transmit, store, and process more relevant data (e.g., data that is used by a machine learning program to make a prediction or a decision). In some cases, incorrect feature importance estimation may fail to explain behavior of the underlying models, such as the decision-making of autonomous vehicles.